3.1 Comparison of SAM classification with LISS III visual
interpretation
The Spectral Angle Mapping of hyperion data and visual interpretation of LISS III is shown in Figure 5. below
Figure 4.
Reference image with SAM classification
Figure 5.
SAM of hyperion and Visual interpretation of LISS III
The result of Spectral Angular Mapping classification showed that there is an increase in area under agricultural and wasteland
classes, when compared with LISS III. In SAM classification a similarity is noted in the reflectance of built up and some
wasteland area like sandybarren area. Hence, there is a possibility of over classification of some of the classes.
Vegetationcrop classes were very distinct with absorption band in blue 430450 and red 650 reflectance peak in green 550,
the red edge and the near-infrared and short-wave infrared region with its typical water vibrational absorption features. A
part from the routine 54 classes, more vegetation classes can be distinguished from the image.
Table 1. Error matrix table for LISS III
Table 2. Error matrix table for Hyperion data Overall accuracy assessment of both images with random
points from high resolution data showed that both had same accuracy of 82 15 sample points were correct out of 18
random points and the classes misinterpretation of the images were different. For hyperion there was misinterpretation of
fallow and also between scrub land between crop and fallow. For LISS III there was misinterpretation between built up sparse
and scrub land and also fallow and scrubland. An overlay of visual interpretation of Liss III and SAM
classification provided a better understanding of the type and extent of each land use land cover features.
3.2
Differentiating vegetation from canopy structure
Different class of vegetation wereidentified from the image like plantation, wetland vegetation, grassland and crops at initial and
senescence stage which are depicted in the figure 5 with range from veg1 to veg9. The spectral signature for the different
vegetation are identified and used for spectral library generation and classification of image.
LISS III Aerial
Built up
Compact Built
up Sparse
Crop Fallow
Scrub land
open Water
body Total
Built up Compact
2 2
Built up Sparse
1 1
2 Crop
3 2
1 6
Fallow 1
2 1
4 Scrub
land open
1 2
Waterbo dy
2 2
2 2
4 5
2 2
18
Hyperion Aerial
Built up Compact
Built up
Sparse Crop
Fallow Scrub
land open
Water body
Total Built up
Compact 2
2 Built up
Sparse 2
2 Crop
4 2
6 Fallow
3 1
4 Scrub
land open
2 2
Water body
2 2
Total 2
1 4
5 3
2 18
Legend
Fallow land Built up Rural
Built up Rural vegetation 8
Vegetation7 Vegetation6
Vegetation5 Vegetation4
Vegetation3 Vegetation2
Vegetattion1 Vegetation 9
ISPRS Technical Commission VIII Symposium, 09 – 12 December 2014, Hyderabad, India
This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-8-991-2014
993
Figure 6. Different Vegetation type identified Vegetation which is in the senescence stage can be
distinguished visually from the hyperspectral image. The yellow patches in the true colour compared with the patches in high
resolution data is shown figure.7 below. Figure 7. Hyperspectral image and High resolution image of
crop at senescence
Reflectance of wetlands in hyperspectral image is more when compared with the high resolution data of less than 15m
resolution. Aquatic vegetations was showing more absorption in Short wave infra red region. The algal blooms in the waterbody
are clearly delineated from the hydric part in hyperspectral data Figure 8. Hyperspectral image and High resolution image of
Aquatic vegetation
3.3 Differentiating vegetation from canopy structure
Kategori